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可解释的动态治疗方案AI 解读

Precision medicine is currently a topic of great interest in clinical and intervention science. A key component of precision medicine is that it is evidence-based, i.e., data-driven, and consequently there has been tremendous interest in estimation of precision medicine strategies using observational or randomized study data. One way to formalize precision medicine is through a treatment regime, which is a sequence of decision rules, one per stage of clinical intervention,…

改进不完整数据下总体均值双重稳健估计的效率与稳健性AI 解读

Abstract Considerable recent interest has focused on doubly robust estimators for a population mean response in the presence of incomplete data, which involve models for both the propensity score and the regression of outcome on covariates. The usual doubly robust estimator may yield severely biased inferences if neither of these models is correctly specified and can exhibit nonnegligible bias if the estimated propensity score is close to zero for some observations. We…

病例队列研究的半参数变换模型AI 解读

A general class of semiparametric transformation models is studied for analysing survival data from the case-cohort design, which was introduced by Prentice (1986). Weighted estimating equations are proposed for simultaneous estimation of the regression parameters and the transformation function. It is shown that the resulting regression estimators are asymptotically normal, with variance-covariance matrix that has a closed form and can be consistently estimated by the usual…

删失临床数据中两阶段随机化设计的生存分布半参数有效估计AI 解读

Two-stage randomisation designs are useful in the evaluation of combination therapies where patients are initially randomised to an induction therapy and then, depending upon their response and consent, are randomised to a maintenance therapy. In this paper we derive the best regular asymptotically linear estimator for the survival distribution and related quantities of treatment regimes. We propose an estimator which is easily computable and is more efficient than existing…

失效原因缺失的线性变换竞争风险模型：回归系数的半参数估计AI 解读

We consider the problem of estimating the regression coefficients in a competing risks model, where the relationship between the cause-specific hazard for the cause of interest and covariates is described using linear transformation models and when cause of failure is missing at random for a subset of individuals. Using the theory of Robins et al. (1994) for missing data problems and the approach of Chen et al. (2002) for estimating regression coefficients for linear…

治疗时长可能右删失的观察性时长—响应研究中的半参数推断AI 解读

Once treatment is found to be effective in clinical studies, attention often focuses on optimum or efficacious treatment delivery. In treatment duration-response studies, the optimum treatment delivery refers to the treatment length that optimises the mean response. In many studies, the treatment length is often left to the discretion of an attending investigator or physician but may be abruptly terminated because of treatment-terminating events. Thus, a recommended…

家系研究中单倍型与疾病关联的局部有效稳健估计AI 解读

Modelling human genetic variation is critical to understanding the genetic basis of complex disease. The Human Genome Project has discovered millions of binary DNA sequence variants, called single nucleotide polymorphisms, and millions more may exist. As coding for proteins takes place along chromosomes, organisation of polymorphisms along each chromosome, the haplotype phase structure, may prove to be most important in discovering genetic variants associated with disease.…

半参数效率及其对群组序贯研究设计与分析的意义AI 解读

Authors have shown that the time-sequential joint distributions of many statistics used to analyze data arising from group-sequential time-to-event and longitudinal studies are multivariate normal with an independent increments covariance structure. In Theorem 1 of this article, we demonstrate that this limiting distribution arises naturally when one uses an efficient test statistic to test a single parameter in a semiparametric or parametric model. Because we are able to…

灵活风险率估计量的渐近性质AI 解读

Suppose one has a stochastic time-dependent covariate $Z(t)$, and is interested in estimating the hazard relationship $\lambda(t|\overline{Z}(t)) = \omega(Z(t))$, where $\overline{Z}(t)$ denotes the history of $Z(t)$ up to and including time t. In this paper, we consider a model of the form $\exp(s_n(Z(t)))$, where $s_n(Z(t))$ is a spline of finite but arbitrary order, and investigate the behavior of the maximum likelihood estimator of the hazard as the number of knots in…

摘要暂缺

含时变协变量的加速失效时间模型的半参数估计AI 解读

A class of semiparametric accelerated failure time models is introduced that are useful for modelling the relationship of survival distributions to time dependent covariates. A class of semiparametric rank estimators is derived for the parameters in this model when the survival data are right censored. These estimates are shown to be consistent, and asymptotically normal with variances that can be consistently estimated. It is also shown that estimators within this class…

利用删失数据线性秩检验估计回归参数AI 解读

A class of estimates for regression parameters in a linear model with right censored data is proposed. These estimates are derived by using linear rank tests for right censored data as estimating equations. They are shown to be consistent and asymptotically normal with covariance matrix for which estimates are proposed. Efficient estimates within this class are derived together with conditions when they are fully efficient.

组序贯试验结束后的精确置信区间：方法比较AI 解读

With interest in designing group sequential clinical trials increasing, methodology for analysing the data arising in such trials is needed. We discuss several approaches to constructing confidence intervals for the parameter of interest in a group sequential trial after the trial has ended. We compare these methods to one already in the literature and point out interesting differences.

含替代协变量的广义线性模型中得分检验的渐近相对效率AI 解读

In many epidemiological studies, the exposure variable of interest cannot be measured directly. The classical approaches to errors in variables in regression do not extend easily to the nonlinear models commonly used in epidemiological research. Furthermore, the traditional additive measurement error model cannot adequately represent many surrogate relationships. By considering the effect of using surrogate independent variables on the efficient score statistic, some of the…

组序贯试验结束后的精确置信区间：方法比较AI 解读

With interest in designing group sequential clinical trials increasing, methodology for analysing the data arising in such trials is needed. We discuss several approaches to constructing confidence intervals for the parameter of interest in a group sequential trial after the trial has ended. We compare these methods to one already in the literature and point out interesting differences.

含替代协变量的广义线性模型中得分检验的渐近相对效率AI 解读

In many epidemiological studies, the exposure variable of interest cannot be measured directly. The classical approaches to errors in variables in regression do not extend easily to the nonlinear models commonly used in epidemiological research. Furthermore, the traditional additive measurement error model cannot adequately represent many surrogate relationships. By considering the effect of using surrogate independent variables on the efficient score statistic, some of the…

高度分层数据的修正对数秩检验AI 解读

Clinical trials often employ a stratified randomization so that treatment arms are balanced within prognostic factors. In these circumstances the use of the ordinary log rank test for comparing treatment groups with respect to survival is invalid. The stratified log rank test, although valid, loses efficiency as the number of strata increases. This paper proposes a modified log rank test that is fully efficient when there is no strata effect and is more efficient than the…

适用于高度分层数据的修正对数秩检验AI 解读

Clinical trials often employ a stratified randomization so that treatment arms are balanced within prognostic factors. In these circumstances the use of the ordinary log rank test for comparing treatment groups with respect to survival is invalid. The stratified log rank test, although valid, loses efficiency as the number of strata increases. This paper proposes a modified log rank test that is fully efficient when there is no strata effect and is more efficient than the…

调整协变量的删失生存数据组序贯检验AI 解读

Group sequential tests for the analysis of survival data that compare two treatments and allow for the adjustment of other concomitant variables are discussed. These methods allow early termination of a study if large treatment differences occur. Based on simulation results, we compare the operating characteristics of these adjusted tests with the unadjusted sequentially-computed log rank test.

调整协变量的删失生存数据组序贯检验AI 解读

Group sequential tests for the analysis of survival data that compare two treatments and allow for the adjustment of other concomitant variables are discussed. These methods allow early termination of a study if large treatment differences occur. Based on simulation results, we compare the operating characteristics of these adjusted tests with the unadjusted sequentially-computed log rank test.

删失生存分析中一般统计量的重复显著性检验AI 解读

The asymptotic joint distribution of a class of sequentially computed statistics used in survival analysis is derived when the individuals under study enter serially and are subject to random loss to follow-up. These results are used for constructing repeated significance tests over real time.

Cox 回归模型的大样本研究AI 解读

Strong consistency and asymptotic normality are established for the maximum partial likelihood estimate of the regression parameter in Cox's regression model. Estimates are also derived for the underlying cumulative hazard function and survival distribution. We establish the asymptotic normality of these estimates and calculate the limiting variances.

随时间计算的比例风险模型有效得分检验的渐近联合分布AI 解读

The score statistic of the proportional hazards model calculated at different points in time is shown to converge asymptotically to a multivariate normal process with independent increments when individuals enter randomly throughout the course of the study.

逻辑回归模型拟合优度检验札记AI 解读

A goodness-of-fit test for the logistic regression model is proposed which is asymptotically chi-squared and is computed as a quadratic form of observed counts minus the expected counts.